In a Light Detection and Ranging (LIDAR) based three-dimensional (3D) imaging system, generally dozens/hundreds of photon-detection events are recorded after a series of repeating laser light pulses emitted. Conventionally, the photon-detection events are formed into a time histogram representing the photon detection events at a pixel. Based on the histogram, the laser pulse travelling time is determined, which usually corresponds to a peak of the histogram.
There is, however, is a drawback associated with the conventional approach due to histogram binning. That is, the time of a photon detection event is quantized to one of a finite number of histogram bins, which results in a quantization error. The quantization process also reduces depth resolution, as well as increases quantization error. For example, for a LIDAR camera system that measures distance maximum up to 40 meters, if the histogram includes 280 bins, the depth resolution is 15 cm, and the depth error is about 4 cm. That means that the LIDAR camera system cannot distinguish between objects that are 15 cm apart from the camera system, and will have a depth error of about 4 cm.
Simply increasing the number of bins in the histogram may help increase the depth resolution. Unfortunately, other issues also may occur when the number of bins of a histogram is increased. First, an increase in the hardware resources and physical space are needed to make a bin. An increase in the number of histogram bins dramatically increases the size of a sensor. Secondly, smaller bins make peak detection difficult because a sharp peak that may occur in a histogram having larger bins would be more spread-out in a histogram having smaller bins. As a result the depth error increases.